




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


snake_case_  :      Dict		=		logging.get_logger(__name__)

snake_case_  :      Optional[int]		=		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

snake_case_  :      Tuple		=		{
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

snake_case_  :      List[str]		=		{
    "gpt-neox-20b": 2048,
}





class 					snake_case_   (     _lowerCAmelCase	):



				'''simple docstring'''
				lowerCamelCase       =  VOCAB_FILES_NAMES
				lowerCamelCase       =  PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase       =  ['''input_ids''', '''attention_mask''']





				def __init__(      self							:   List[Any]      ,							__magic_name__							:   List[str]=None      ,							__magic_name__							:   Any=None      ,							__magic_name__							:   int=None      ,							__magic_name__							:   Optional[int]="<|endoftext|>"      ,							__magic_name__							:   Tuple="<|endoftext|>"      ,							__magic_name__							:   Optional[int]="<|endoftext|>"      ,							__magic_name__							:   List[Any]=False      ,							**__magic_name__							:   Union[str, Any]      ,							)							->			List[Any]:
											super().__init__(
											    UpperCAmelCase_      ,							UpperCAmelCase_      ,							tokenizer_file=UpperCAmelCase_      ,							unk_token=UpperCAmelCase_      ,							bos_token=UpperCAmelCase_      ,							eos_token=UpperCAmelCase_      ,							add_prefix_space=UpperCAmelCase_      ,							**UpperCAmelCase_      ,							)

											lowerCamelCase_			:    Tuple   						=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
											if pre_tok_state.get("add_prefix_space"      ,							UpperCAmelCase_							) != add_prefix_space:
																		lowerCamelCase_			:    List[str]   						=      getattr(UpperCAmelCase_      ,							pre_tok_state.pop("type"							)							)
																		lowerCamelCase_			:    Optional[Any]   						=      add_prefix_space
																		lowerCamelCase_			:    Optional[Any]   						=      pre_tok_class(**UpperCAmelCase_							)

											lowerCamelCase_			:    int   						=      add_prefix_space





				def    __SCREAMING_SNAKE_CASE     (      self							:   int      ,							__magic_name__							:   str      ,							__magic_name__							:   Optional[str] = None							)							->			Tuple[str]:
											lowerCamelCase_			:    Dict   						=      self._tokenizer.model.save(UpperCAmelCase_      ,							name=UpperCAmelCase_							)
											return tuple(UpperCAmelCase_							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   int      ,							__magic_name__							:   "Conversation"							)							->			List[int]:
											lowerCamelCase_			:    int   						=      []
											for is_user, text in conversation.iter_texts():
																		input_ids.extend(self.encode(UpperCAmelCase_      ,							add_special_tokens=UpperCAmelCase_							) + [self.eos_token_id]							)

											if len(UpperCAmelCase_							) > self.model_max_length:
																		lowerCamelCase_			:    Dict   						=      input_ids[-self.model_max_length :]
											return input_ids



"""simple docstring"""


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				float							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				bool = False							,   )->	str:







              """simple docstring"""
              super().__init__()

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		False

              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )

              UpperCamelCase       			=		TaConfig(
                  vocab_size=UpperCAmelCase_							,   d_model=UpperCAmelCase_							,   num_heads=UpperCAmelCase_							,   d_kv=UpperCAmelCase_							,   d_ff=UpperCAmelCase_							,   dropout_rate=UpperCAmelCase_							,   feed_forward_proj=UpperCAmelCase_							,   is_decoder=UpperCAmelCase_							,   is_encoder_decoder=UpperCAmelCase_							,   )

              UpperCamelCase       			=		nn.ModuleList()
              for lyr_num in range(UpperCAmelCase_      ):
                     UpperCamelCase       			=		TaBlock(UpperCAmelCase_      )
                     self.encoders.append(UpperCAmelCase_      )

              UpperCamelCase       			=		TaLayerNorm(UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.token_embedder(UpperCAmelCase_      )

              UpperCamelCase       			=		encoder_input_tokens.shape[1]
              UpperCamelCase       			=		torch.arange(UpperCAmelCase_							,   device=encoder_input_tokens.device      )
              x += self.position_encoding(UpperCAmelCase_      )

              UpperCamelCase       			=		self.dropout_pre(UpperCAmelCase_      )

              # inverted the attention mask
              UpperCamelCase       			=		encoder_input_tokens.size()
              UpperCamelCase       			=		self.get_extended_attention_mask(UpperCAmelCase_							,   UpperCAmelCase_      )

              for lyr in self.encoders:
                     UpperCamelCase       			=		lyr(UpperCAmelCase_							,   UpperCAmelCase_      )[0]
              UpperCamelCase       			=		self.layer_norm(UpperCAmelCase_      )

              return self.dropout_post(UpperCAmelCase_      ), encoder_inputs_mask





